set -x

# If you are using vllm<=0.6.3, you might need to set the following environment variable to avoid bugs:
# export VLLM_ATTENTION_BACKEND=XFORMERS

python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=grpo \
    data.train_files=$DATA_DIR/train.parquet \
    data.val_files=$DATA_DIR/test.parquet \
    data.train_batch_size=$BATCH_SIZE \
    data.max_prompt_length=512 \
    data.max_response_length=$MAX_RESPONSE_LENGTH \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    data.shuffle=False \
    actor_rollout_ref.model.path=$BASE_MODEL \
    actor_rollout_ref.model.use_shm=True \
    actor_rollout_ref.model.lora_rank=64 \
    actor_rollout_ref.model.lora_alpha=32 \
    actor_rollout_ref.actor.optim.lr=$LEARNING_RATE \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=$BATCH_SIZE \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=40 \
    actor_rollout_ref.actor.use_kl_loss=True \
    actor_rollout_ref.actor.kl_loss_coef=0.001 \
    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=False \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=40 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=$ROLLOUT_TP_SIZE \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
    actor_rollout_ref.rollout.n=$ROLLOUT_N \
    actor_rollout_ref.rollout.load_format=safetensors \
    actor_rollout_ref.rollout.layered_summon=True \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=40 \
    actor_rollout_ref.ref.fsdp_config.param_offload=True \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.logger=['swanlab'] \
    trainer.project_name=$PROJECT_NAME \
    trainer.experiment_name=$EXPERIMENT_NAME \
    trainer.n_gpus_per_node=$N_GPUS \
    trainer.nnodes=1 \
    trainer.save_freq=20 \
    trainer.test_freq=5 \
    trainer.validation_data_dir=$OUTPUT_DIR/validation \
    trainer.rollout_data_dir=$OUTPUT_DIR/rollout \
    trainer.total_epochs=15 2>&1 | tee $LOG_PATH/log/$EXPERIMENT_NAME.log